Measuring Soil Surface Changes after Traffic of Various Wheeled Skidders with Close-Range Photogrammetry

Abstract

Soil surface is directly affected by heavy traffic of machinery during harvesting operations. Machine traffic often causes damage to forest soil which is visible on the surface (ruts) and invisible changes in, for example, bulk density, penetration resistance, etc. Close-range photogrammetry is the state-of-the-art method used for recording and evaluation of visible changes. This study aims to analyze soil surface changes caused by traffic of three types of wheeled skidders without a load on Cambisol soil in Central Slovakia. We use the Structure-from-Motion (SfM) close-range photogrammetry to record and evaluate depths of ruts and their volumes after 40 passes of individual skidders. We compared Root Mean Square Errors (RMSEs) of dense point clouds created from various numbers of images taken for individual plots. Rut volume changes calculated by the SfM method and from the manual measurements were compared for one skidder. The final values of RMSE did not exceed 10 mm except for the plot with the lowest number of photos. The final rut depths varied between 0.026 and 0.050 m, and their final volume fluctuated from 0.021 to 0.089 m3. The skidder type and the terrain slope had significant impacts on magnitudes of soil changes. The results of the manual and SfM methods assessing soil changes were correlated. Based on the presented results we can conclude that the SfM method can be applied to detect soil surface changes after traffic of forestry machinery.

Identification of wheel track in the wheat field

“…When comparing the calculated outcomes to those of previous research, it was found that Salmivaara et al 37 achieved a root RMSE of 0.035 m when employing a Light Detection and Ranging (LiDAR) sensor to measure the depth of wheel tracks. In their investigation of soil surface changes using close-range photogrammetry, Ferencík et al 26 found a range of RMSE between 0.026 m and 0.050 m. The RMSE values calculated for this investigation's row and column coordinates were smaller than the reported values.…”

“…Liu et al 29 optimized the asphalt mix design using rut depth prediction for asphalt pavement. In their study, Ferenčík et al 26 utilized Close-range photogrammetry to quantify the depth of the rut formed by wheeled skidders within the University Forest Enterprise of the Technical University located in central Slovakia. The study by Zheng et al 30 examined the impact of rut in asphalt surfaces on the steering stability of autonomous vehicles.…”

Recent Applications of Smart Technologies for Monitoring the Sustainability of Forest Operations

“…In the recent literature, two other precision forestry approaches for the monitoring of soil disturbances after ground-based forest operations can be found; these are Structure-from-Motion (SfM) Photogrammetry and LiDAR [82,83]. Photogrammetry consists of a technique in which measurements made from one or more images are used to estimate the three-dimensional coordinates of points on a surface.…”

Methods of Rut Depth Measurements on Forwarder Trails in Lowland Forest

“…Salmivaara et al [31] investigated the applicability of the 2D LiDAR system when measuring ruts and determined that the average error of this system is 3.5 cm. Water in the ruts makes it difficult to use photogrammetric methods and LiDAR technology and is a prominent problem in the studies mentioned and others [32][33][34]. Depending on the water depth, the error can be up to 15 cm [28].…”